Scene adaptive brightness/contrast enhancement

ABSTRACT

A method for brightness and contrast enhancement includes computing a luminance histogram of a digital image, computing first distances from the luminance histogram to a plurality of predetermined luminance histograms, estimating first control point values for a global tone mapping curve from predetermined control point values corresponding to a subset of the predetermined luminance histograms selected based on the computed first distances, and interpolating the estimated control point values to determine the global tone mapping curve. The method may also include dividing the digital image into a plurality of image blocks, and enhancing each pixel in the digital image by computing second distances from a pixel in an image block to the centers of neighboring image blocks, and computing an enhanced pixel value based on the computed second distances, predetermined control point values corresponding to the neighboring image blocks, and the global tone mapping curve.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/049,609, filed Mar. 16, 2011, which application claims benefit ofU.S. Provisional Patent Application Ser. No. 61/314,929, filed Mar. 17,2010, both of which are incorporated by reference herein in theirentirety.

BACKGROUND OF THE INVENTION

Imaging and video capabilities have become the trend in consumerelectronics. Digital cameras, digital camcorders, and video cellularphones are common, and many other new gadgets are evolving in themarket. Advances in large resolution CCD/CMOS sensors coupled with theavailability of low-power digital signal processors (DSPs) has led tothe development of digital cameras with both high resolution image andshort audio/visual clip capabilities. The high resolution (e.g., sensorwith a 2560×1920 pixel array) provides quality offered by traditionalfilm cameras.

As the camera sensor and signal processing technologies advanced, thenominal performance indicators of camera performance, e.g., picturesize, zooming, and range, reached saturation in the market. Then, endusers shifted their focus back to actual or perceivable picture quality.The criteria of users in judging picture quality include signal to noiseratio (SNR) (especially in dark regions), blur due to hand shake, blurdue to fast moving objects, natural tone, natural color, etc.

The perceived quality of still images and video is heavily influenced byhow brightness/contrast of a scene is rendered, which makesbrightness/contrast enhancement (BCE) one of the fundamental parts of animage pipeline. BCE is a challenging problem because human perception ofbrightness/contrast is quite complex and is highly dependent on thecontent of a still image or video frames. Many current BCE methods donot adequately address this complexity. When tested on large sets ofimages, these methods may fail in certain scenes (e.g., flat objects,clouds in a sky) because image content is very diverse. That is, manycurrent BCE methods apply a fixed technique to all images/framesregardless of content and, as a result, may produce poor quality resultson some images/frames because they do not adapt to content variation.Accordingly, improvements in BCE techniques are desirable.

BRIEF DESCRIPTION OF THE DRAWINGS

Particular embodiments in accordance with the invention will now bedescribed, by way of example only, and with reference to theaccompanying drawings:

FIG. 1 shows a digital system in accordance with one or more embodimentsof the invention;

FIG. 2 shows a block diagram of an image processing pipeline inaccordance with one or more embodiments of the invention;

FIGS. 3A and 3B show a flow graph of a training method for globalbrightness and contrast enhancement in accordance with one or moreembodiments of the invention;

FIG. 3C shows an example of manual tuning in the training method ofFIGS. 3A and 3B in accordance with one or more embodiments of theinvention;

FIG. 4 shows a flow graph of a method for global brightness and contrastenhancement in accordance with one or more embodiments of the invention;

FIG. 5 shows a flow graph of a training method for local brightness andcontrast enhancement in accordance with one or more embodiments of theinvention;

FIG. 6 shows a flow graph of a method for local brightness and contrastenhancement in accordance with one or more embodiments of the invention;and

FIGS. 7 and 8 show illustrative digital systems in accordance with oneor more embodiments.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

Specific embodiments of the invention will now be described in detailwith reference to the accompanying figures. Like elements in the variousfigures are denoted by like reference numerals for consistency.

Certain terms are used throughout the following description and theclaims to refer to particular system components. As one skilled in theart will appreciate, components in digital systems may be referred to bydifferent names and/or may be combined in ways not shown herein withoutdeparting from the described functionality. This document does notintend to distinguish between components that differ in name but notfunction. In the following discussion and in the claims, the terms“including” and “comprising” are used in an open-ended fashion, and thusshould be interpreted to mean “including, but not limited to . . . . ”Also, the term “couple” and derivatives thereof are intended to mean anindirect, direct, optical, and/or wireless electrical connection. Thus,if a first device couples to a second device, that connection may bethrough a direct electrical connection, through an indirect electricalconnection via other devices and connections, through an opticalelectrical connection, and/or through a wireless electrical connection.

In the following detailed description of embodiments of the invention,numerous specific details are set forth in order to provide a morethorough understanding of the invention. However, it will be apparent toone of ordinary skill in the art that the invention may be practicedwithout these specific details. In other instances, well-known featureshave not been described in detail to avoid unnecessarily complicatingthe description. In addition, although method steps may be presented anddescribed herein in a sequential fashion, one or more of the steps shownand described may be omitted, repeated, performed concurrently, and/orperformed in a different order than the order shown in the figuresand/or described herein. Accordingly, embodiments of the inventionshould not be considered limited to the specific ordering of steps shownin the figures and/or described herein.

As used herein, the term digital image data refers to pixels in adigital image. A digital image may be a single still digital image or aframe (or subset of a frame such as, for example, a slice, a field, avideo object plane, a picture, etc.) of digital image data in a digitalvideo sequence. The term digital image data may also refer to luminancedata and/or chrominance data of pixels. For example, a block of digitalimage data may be composed of pixels having both luminance data andchrominance data, pixels having only luminance data, or pixels havingonly chrominance data.

Embodiments of the invention provide for BCE that uses a set ofprototypes to adapt the BCE behavior to different scene types. Ingeneral, given a new digital image, the BCE interpolates from the set ofprototypes to determine the best brightness/contrast enhancement for thedigital image. The set of prototypes is developed from a set ofrepresentative digital images that includes a wide array of scene typesand is stored for use by the BCE. Thus, the BCE can be tuned for manydifferent scene types. Using a set of prototypes to adapt BCE fordifferent scene types improves brightness/contrast enhancement for allscene types.

In some embodiments of the invention, global brightness/contrastenhancement (GBCE) using a set of prototypes designed for GBCE isprovided. In other embodiments of the invention, localbrightness/contrast enhancement (LBCE) using a set of prototypesdesigned for LBCE is provided. In such embodiments, both GBCE and LBCEare performed. Further, the LBCE prototypes are generated using the GBCEprototypes. In general, LBCE operates on small areas, e.g., subsets orblocks of a digital image, while GBCE operates on the entire digitalimage. Tests have shown that applying both GBCE and LBCE to a digitalimage outperforms the use of GBCE alone in almost all cases. However,LBCE is more computationally complex.

FIG. 1 shows a digital system suitable for an embedded system inaccordance with one or more embodiments of the invention that includes,among other components, a DSP-based image coprocessor (ICP) (102), aRISC processor (104), and a video processing engine (VPE) (106). TheRISC processor (104) may be any suitably configured RISC processor. TheVPE (106) includes a configurable video processing front-end (Video FE)(108) input interface used for video capture from imaging peripheralssuch as image sensors, video decoders, etc., a configurable videoprocessing back-end (Video BE) (110) output interface used for displaydevices such as SDTV displays, digital LCD panels, HDTV video encoders,etc, and memory interface (124) shared by the Video FE (108) and theVideo BE (110). The digital system also includes peripheral interfaces(112) for various peripherals that may include a multi-media card, anaudio serial port, a Universal Serial Bus (USB) controller, a serialport interface, etc.

The Video FE (108) includes an image signal processor (ISP) (116), and a3A statistic generator (3A) (118). The ISP (116) provides an interfaceto image sensors and digital video sources. More specifically, the ISP(116) may accept raw image/video data from a sensor (CMOS or CCD) andcan accept YUV video data in numerous formats. The ISP (116) alsoincludes a parameterized image processing module with functionality togenerate image data in a color format (e.g., RGB) from raw CCD/CMOSdata. The ISP (116) is customizable for each sensor type and supportsvideo frame rates for preview displays of captured digital images andfor video recording modes. The ISP (116) also includes, among otherfunctionality, an image resizer, statistics collection functionality,and a boundary signal calculator. The 3A module (118) includesfunctionality to support control loops for auto focus, auto whitebalance, and auto exposure by collecting metrics on the raw image datafrom the ISP (116) or external memory. In one or more embodiments of theinvention, the Video FE (108) is configured to perform at least one ofthe BCE methods as described herein.

The Video BE (110) includes an on-screen display engine (OSD) (120) anda video analog encoder (VAC) (122). The OSD engine (120) includesfunctionality to manage display data in various formats for severaldifferent types of hardware display windows and it also handlesgathering and blending of video data and display/bitmap data into asingle display window before providing the data to the VAC (122) inYCbCr format. The VAC (122) includes functionality to take the displayframe from the OSD engine (120) and format it into the desired outputformat and output signals required to interface to display devices. TheVAC (122) may interface to composite NTSC/PAL video devices, S-Videodevices, digital LCD devices, high-definition video encoders, DVI/HDMIdevices, etc.

The memory interface (124) functions as the primary source and sink tomodules in the Video FE (108) and the Video BE (110) that are requestingand/or transferring data to/from external memory. The memory interface(124) includes read and write buffers and arbitration logic.

The ICP (102) includes functionality to perform the computationaloperations required for compression and other processing of capturedimages. The video compression standards supported may include one ormore of the JPEG standards, the MPEG standards, and the H.26x standards.In one or more embodiments of the invention, the ICP (102) is configuredto perform computational operations for a BCE method as describedherein.

In operation, to capture an image or video sequence, video signals arereceived by the video FE (108) and converted to the input format neededto perform video compression. Prior to the compression, a BCE method asdescribed herein may be applied as part of processing the captured videodata. The video data generated by the video FE (108) is stored in theexternal memory. The video data is then encoded, i.e., compressed.During the compression process, the video data is read from the externalmemory and the compression computations on this video data are performedby the ICP (102). The resulting compressed video data is stored in theexternal memory. The compressed video data is then read from theexternal memory, decoded, and post-processed by the video BE (110) todisplay the image/video sequence.

FIG. 2 is a block diagram illustrating digital camera control and imageprocessing (the “image pipeline”) in accordance with one or moreembodiments of the invention. One of ordinary skill in the art willunderstand that similar functionality may also be present in otherdigital devices (e.g., a cell phone, PDA, etc.) capable of capturingdigital images and/or digital video sequences. While each stage in thepipeline is shown in sequence, one of ordinary skill in the art willunderstand that image processing is parallel in nature and thus, eachstage in the pipeline does not necessarily receive an entire digitalimage before processing can begin. Further, one of ordinary skill in theart will understand that that an image pipeline may contain more orfewer stages, and/or may vary in order. A brief description of thefunction of each stage in accordance with one or more embodiments isprovided below. Note that the typical color CCD consists of arectangular array of photosites (pixels) with each photosite covered bya filter (the CFA): typically, red, green, or blue. In the commonly-usedBayer pattern CFA, one-half of the photosites are green, one-quarter arered, and one-quarter are blue.

As shown in FIG. 2, the initial stage of the pipeline, black leveladjustment, sets sensor black to image black. That is, in order tooptimize the dynamic range of the pixel values represented by a CCDimager, the pixels representing black are corrected since the CCD cellmay record some non-zero current at these pixel locations. The blacklevel adjustment adjusts for this difference by subtracting offsets fromeach pixel value while clamping/clipping to zero to avoid a negativeresult. One simple way to calculate this adjustment is to take a pictureof complete black, e.g., by leaving on the lens cap or camera cover. Theresult of the calculation is the three base offsets that characterizethe dark current from the sensor and lens flare. Failure to subtractthese offsets may have a negative effect on contrast in the final image.

The noise reduction stage attempts to remove the various sources ofnoise in a digital image, e.g., optical, electrical, digital and power,by averaging similar neighboring pixels. Typically, if the noise levelis high then more weight is given to the average of similar neighbors.Conversely, if the noise level is low, more weight is given to theoriginal pixel value. An Optical Electrical Conversion Function (OECF)chart captured using a uniform lighting source may be used to determinethe noise level for different intensities. The 12 uniform gray patcheson the OECF chart provide 12 power levels, which may then be used toarrange noise using either a linear or square-root model depending onthe sensor and gain (or ISO) level.

The white balance stage attempts to make white white in a digital imageby automatically compensating for color differences introduced by lightsources, such as the differences between incandescent, fluorescent,natural light sources, XE strobe, and W-LED flash, as well as mixedlight conditions. That is, the illumination during the recording of ascene in a digital image may be different from the illumination whenviewing the final digital image. This difference may result in adifferent color appearance that may be seen, for example, as the bluishappearance of a face or the reddish appearance of the sky. Also, thesensitivity of each color channel varies such that grey or neutralcolors may not be represented correctly.

In some embodiments of the invention, the white balance functioncompensates for these imbalances in colors by computing the averagebrightness of each color component and by determining a scaling factorfor each color component. Since the illuminants are unknown, afrequently used technique just balances the energy of the three colors.This equal energy approach requires an estimate of the unbalance betweenthe color components. In some embodiments of the invention, theappropriate gains needed for white balance may be calculated using aColorChecker chart to obtain the average RGB values of six gray patches.The red and blue gains may be computed using mean square errorminimization while the green gain may be set to one, eliminating one setof calculations.

Many digital cameras have a single sensor and thus are limited toobtaining one color component at each pixel in a digital image eventhough three components are needed to represent RGB color. However, manystage of the image pipeline need full color resolution for each pixel ina digital image. The CFA interpolation stage interpolates the twomissing color components for each pixel from neighboring pixels.

The RGB blending stage is included because different sensors may producedifferent RGB values for the same color. This stage uses a matrix thatconverts the sensor RGB color space to a standard RGB color space suchas the Rec709 RGB color space. The matrix may be determined using animage of a ColorChecker chart to obtain average RGB values of 18-colorpatches after white balancing, and then using constrained minimizationon reference RGB values after applying an inverse Gamma correction.

The gamma correction stage compensates for the nonlinearity of outputdisplay devices and printers, i.e., it compensates for the differencesbetween a digital image generated by the CCD sensor and the digitalimage displayed on a display device or printed on a page. A standardgamma correction such as Rec709 or SMPTE240M may be used to calibratethis stage. Special displays, such as those used in airport computers ormilitary applications, may require nonstandard gamma correction. In suchcases, a particular gamma correction lookup table for the specialdisplay may be used for gamma correction.

Human eyes are more sensitive to luminance (Y) than color (Cb, Cr)information, and so each component may be processed using differentprecisions. The RGB to YCC conversion stage transforms a digital imagefrom an RGB color space to a YCbCr color space. This conversion may be alinear transformation of each Y, Cb, and Cr value as a weighted sum ofthe R, G, and B values of a pixel.

The nature of CFA interpolation filters introduces a low-pass filterthat smoothes the edges in the image. To sharpen a digital image, theedge enhancement stage detects and enhances the edges. The edgeenhancement may compute the edge magnitude in the Y channel at eachpixel, and then scale the edge magnitude and add it to the originalluminance (Y) image to enhance the sharpness of the edges.

The contrast enhancement stage enhances the brightness and contrast of adigital image. This stage performs brightness and contrast enhancementaccording to a method for BCE as described herein.

The false chroma suppression stage corrects various color artifacts. Forexample, edge enhancement is only performed in the Y channel of adigital image. This may cause misalignment in the color channels at theedges, resulting in rainbow-like artifacts. The false chroma suppressionstage suppresses the color components, Cb and Cr, at the edges to reducesuch artifacts.

Embodiments of methods for brightness and contrast enhancement (BCE) arenow described in more detail under the headings of GlobalBrightness/Contrast Enhancement (GBCE) and Local Brightness/ContrastEnhancement (LBCE). Embodiments of the GBCE method and the LBCE methodoperate using sets of prototypes that are pre-generated and stored in adigital camera or other devices that enhance contrast in captureddigital images. Accordingly, embodiments of training methods forgenerating GBCE prototypes and LBCE prototypes are also described underthe corresponding headings.

Global Brightness/Contrast Enhancement (GBCE)

FIGS. 3A and 3B show a flow diagram of a training method for generatingprototypes for GBCE in accordance with one or more embodiments of theinvention. The training method uses a set of training images that arerepresentative of scenes typically captured using a digital camera. Alarge and diverse set of training images may be used to improve thecontent of the generated prototypes. In some embodiments of theinvention, approximately a thousand images are used for the training.

Initially, a luminance histogram is computed for each of the trainingimages (300). Any suitable technique for computing the luminancehistogram may be used. The number of bins in the luminance histogramdepends on the dynamic range of the luminance image. For simplicity ofexplanation, this dynamic range is assumed to be 0-255 herein such thatthe luminance histograms have 256 bins. One of ordinary skill in the artwill understand other embodiments of the invention in which the dynamicrange is different. Further, the luminance histograms are normalizedwith the number of pixels in the digital image such that the sum of aluminance histogram, i.e., the sum of all the bin values of a histogram,is the same for all of the training images (302). The sum of a luminancehistogram is referred to as histSum herein.

A subset of the training images, i.e., the training prototype set, isthen determined based on the normalized luminance histograms. To selectthe training prototype subset, a training image from the full set oftraining images is selected (306). The training images may be processedin any suitable order. If the training prototype set is empty (306),then the selected training image is added to the training prototype set(316), and processing continues with the next training image in the fullset of training images (304)), if any (316).

If the training prototype set is not empty (306), then the selectedtraining image is considered for addition to the training prototype set.The distance between the luminance histogram of the selected trainingimage and each of the luminance histograms of the images in the trainingprototype set is computed (308). The distance may be computed using anysuitable technique, such as for example, Euclidean distance orchi-squared distance. In some embodiments of the invention, eachdistance is computed as the sum of absolute differences (SAD) betweenthe luminance histogram of the selected training image and a luminancehistogram of an image in the training prototype set.

The minimum distance of the computed distances is then determined (310),and if this minimum distance is larger than a threshold, the selectedtraining image is added to the training prototype set (314). Otherwise,the selected training image is not added to the training prototype set,and processing continues with the next training image in the full set oftraining images (304), if any (316). In some embodiments of theinvention, the threshold is 0.9*histSum.

After all of the training images in the full set of images areconsidered (316), the training prototype set is complete. The luminancehistograms of the images in the training prototype set are referred toas H1, H2, . . . , Hn herein where n is the number of images in thetraining prototype set. In some embodiments of the invention, n isaround 500.

After the training prototype set is complete (316), each of the trainingimages in the training prototype set is tuned based on a tuning tonemapping curve (318). This tuning is performed manually by a few people.For tuning of each image, the original image, the luminance histogramfor that image, a tuning tone mapping curve with m adjustable controlpoints, and an output image showing the effects of adjustments tocontrol points in the tuning tone mapping curve on brightness andcontrast may be displayed. A user may tune each of the training imagesby adjusting the m control points in the tuning tone mapping curve upand down to achieve the desired brightness/contrast enhancement. FIG. 3Cshows an example of such an interface. In each of the tuning tonemapping curves in this figure, the dashed straight line is the unitytone mapping curve and the curved solid line is the tone mapping curveas adjusted by the user. The user may refer to the luminance histogramfor a training image in deciding how what changes should be made to thetone mapping curve for that training image.

The result of the tuning of each training image is a control pointvector of length m that specifies the location of each of the m controlpoints in the final tone mapping curve selected by a user. In someembodiments of the invention, m=10 and the 10 control points are locatedat 0, 25, 64, 96, 128, 160, 192, 210, 225, and 256 in each tuning tonemapping curve. The locations of the control points selected by each userfor each training image may be averaged to estimate an optimal tonemapping curve, i.e., an optimal control point vector, which describesthe brightness/contrast preferences for that training image over theuser population. The final control point vectors are referred to hereinas V1, V2, . . . , Vn, where n is the number of training images in thetraining prototype set.

Optionally, dimensionality reduction and/or pruning techniques may beapplied to the prototype set to reduce the amount of memory needed tostore the prototype set (320). For example, principal component analysis(PCA) may be applied (320) to the luminance histograms H1, H2, . . . ,Hn to reduce the dimensionality of the histograms, and thus the amountof memory needed to store them. PCA provides eigenvalues andeigenvectors. A threshold may be applied to the eigenvalues to determinethe z dominant eigenvalues and the corresponding eigenvectors. In someembodiments of the invention, z is around 50. The n prototype histogramsare projected onto the z eigenvectors to determine feature vectors F1,F2, . . . , Fn of length z. Note that each original histogram vector isof length 256 and after the application of PCA, a smaller vector of sizez may be used to represent the original histogram. Pruning may also beperformed to eliminate similar histogram/tone mapping curve pairs tofurther reduce the amount of data to be stored.

In embodiments of the invention where PCA is not applied, the output ofthe GBCE training is a GBCE prototype set that includes a luminancehistogram vector of length 256 and a control point vector for each ofthe n training images in the prototype set. In embodiments of theinvention where PCA is applied, the output of the GBCE training is aGBCE prototype set that includes a luminance histogram vector of lengthz and a control point vector for each of the n training images in theprototype set. If pruning is applied to the training prototype set, thenumber of luminance histogram/control point vector pairs in the GBCEprototype set will naturally be less than n. The GBCE prototype set maythen be stored for use in the BCE methods of FIGS. 4 and 6.

Optionally, the training images in the training prototype set may begrouped based on scene type, such as outdoor, indoor, dark, etc. Thegrouping may be performed based on auxiliary data from the camera usedto capture the training images such as exposure time and analog gain.The grouping may be used to reduce the search space when comparing ahistogram of a training image to the training prototype set. Further,the grouping may facilitate better matching to the respective scenes andmay result in picking the best tone curve for that scene.

FIG. 4 is a flow diagram of a method for BCE in accordance with one ormore embodiments of the invention. The method uses the output of thetraining method of FIGS. 3A-3B, i.e., a GBCE prototype set withluminance histogram/control point vector pairs. The description of thismethod assumes that there are n luminance histogram/control point vectorpairs as per the description of the training method of FIGS. 3A-3B. Oneof ordinary skill in the art will understand embodiments of theinvention where there are fewer than n pairs in the GBCE prototype set,i.e., where some pairs were pruned out during training.

Initially, a digital image is received (400) and a luminance histogramis computed for the image (402), referred to herein as Hnew. In someembodiments of the invention, a 256 bin luminance histogram is computed.Further, the luminance histogram is normalized in a similar fashion asthe normalization used for the luminance histograms in the trainingmethod. The distance between the luminance histogram Hnew and each ofthe luminance histograms in the GBCE prototype set H1, H2, . . . , Hn isthen computed (404). The distances are computed in the same way as thedistances were computed when the GBCE prototype set was generated. Insome embodiments of the invention, each distance is computed as the sumof absolute differences (SAD) between the luminance histogram of thedigital image and a luminance histogram in the GBCE prototype set. Ifprincipal component analysis was used in the generation of the GBCEprototype set to reduce dimensionality, then the distance is computed byprojecting Hnew onto the z eigenvectors and then the distance iscomputed to each of the feature vectors F1, F2, . . . , Fn.

The computed distances are then ranked from the smallest to the largestand a predetermined number M of the smallest distance are selected(406). The ranked distances are referred to herein as d1, d2, . . . ,dn, where d1 is the smallest distance. The value of M may be empiricallydetermined. In some embodiments of the invention, the value of M mayrange from 2 to 4.

Then, m control points for a tone mapping curve for the digital image,i.e., a control point vector referred to herein as Vnew, are estimatedbased on the control point vectors V1 . . . VM associated with theluminance histograms H1 . . . HM in the GBCE prototype set correspondingto the M smallest distances (408). In one or more embodiments of theinvention, the m control points in Vnew are estimated as the weightedaverage of the m control points in V1 . . . VM. The weights used in thecomputation are chosen such that V1 has the greatest weight and VM hasthe least weight. More specifically, the weights for each of the Mcontrol point vectors are computed as follows. The equation(k/d1)+(k/d2)+ . . . +(k/dM)=1 is solved to determine a value of k. Notethat this equation ensures that the weights used will add up to one.Then, the weights are computed as alfa1=k/d1, alfa2=k/d2, . . . ,alfaM=k/dM, where alfa1 . . . alfaM are the weights. The m controlpoints of Vnew are then estimated by computing Vnew=alfa1*V1+alfa2*V2+ .. . +alfaM*VM.

The final tone mapping curve is then determined by interpolating the mcontrol points in Vnew (410). In some embodiments of the invention,polynomial interpolation such as cubic polynomial interpolation is used.The final tone mapping curve is then applied to the digital image toenhance the brightness/contrast (412).

Local Brightness/Contrast Enhancement (LBCE)

FIG. 5 shows a flow diagram of a training method for generating controlpoints of tone mapping curves for LBCE in accordance with one or moreembodiments of the invention. The training method is performed using thetraining images in the GBCE training prototype set. Accordingly, theGBCE training method of FIGS. 3A and 3B is performed first. The trainingimages in the GBCE training prototype set are then enhanced using theGBCE method of FIG. 4. The resulting enhanced training images form thetraining prototype set for the LBCE training.

Initially, a training image is selected from the training prototype set(500) and divided into image blocks (502). The training images may beprocessed in any suitable order. Further, any suitable image block sizemay be used. In some embodiments of the invention, the training image isdivided into 209 blocks. Then, the mean luminance value, blkMean, andthe mean absolute luminance deviation, blkMAD, are computed for each ofthe image blocks (504). The blkMean may be computed by summing the pixelvalues an image block and taking the average. The blkMAD of an imageblock may be computed as follows. First determine the differencesbetween the blkMean and each luminance value in the image data block.Then, compute the absolute values of all the differences, and averagethe absolute differences. The dividing of training images into imageblocks and computation of the blkMean and blkMAD for each image block isrepeated for all training images in the prototype set (506).

At this point, the training prototype set contains the image blocks forthe training images in the original training prototype set. Each imageblock is the training prototype set is tuned based on a tuning tonemapping curve with p control points, where p<m (the number of controlpoints used to tune the prototype training images in the GBCE trainingmethod). For tuning of each image block, the original image block, atuning tone mapping curve with p adjustable control points, and anoutput image block showing the effects of adjustments to control pointsin the tuning tone mapping curve on brightness and contrast may bedisplayed. A user may tune each of the prototype image blocks byadjusting the p control points in the tuning tone mapping curve up anddown to achieve the desired brightness/contrast enhancement.

The result of the tuning of each image block is p control points valuescorresponding to the locations of each of the p control points in thefinal tone mapping curve selected by a user. The p control point valuesare referred to as V1 i, V2 i, . . . , Vpi herein, where i identifiesthe image block. In some embodiments of the invention, p=3 and the 3control points are located at blkMean/2, blkMean,(256-blkMean/2)+blkMean in each tone mapping curve. The locations of thecontrol points selected by each user for each image block may beaveraged to estimate an optimal tone mapping curve, i.e., an optimal setof p control point values, which describes the brightness/contrastpreferences for that image block over the user population.

The p control point values for each tuned image block are stored inrespective control point tables indexed by the mean luminance value(blkMean) and the mean absolute luminance deviation (blkMAD) (510). Insome embodiments of the invention, the dimensions of the control pointtables are 256×128. More specifically, there are p control point tables,T1, T2, . . . , Tp and all the V1 i values are stored in T1, all the V2i values are stored in T2, etc. Further, the control point values for animage block are stored in the respective control point tables at alocation determined by the blkMean and blkMAD for that image block. Forexample, if p=3, three two-dimensional control point tables, T1, T2, andT3, are used: one for V1 i, one for V2 i, and one for V3 i. Each ofthese three tables is indexed by blkMean and blkMAD. Locations in T1,T2, T3 store the V1 i, V2 i, and V3 i values, respectively, that werecomputed for the image blocks. In some embodiments of the invention, iftwo images blocks have the same blkMean and blkMAD, they are spatiallyaveraged.

The p control point tables may not be fully populated as there may notbe sufficient image blocks in the prototype set for every possibleoccurrence of (blkMean,blkMAD) values. Thus, the missing values in the pcontrol point tables are interpolated using nearby values (512). Themissing values may be interpolated as follows. For each missing value inthe table, find the smallest v×v neighborhood having values populatedduring the training and average the populated values to compute themissing value. The output of the LBCE training is p fully populatedcontrol point tables that are stored and used for the BCE method of FIG.6.

FIG. 6 is a flow diagram of a method for BCE in accordance with one ormore embodiments of the invention. Initially, a digital image isreceived and global tone mapping curve is determined for the digitalimage as per steps 402-410 of the BCE method of FIG. 4 (600). Thedigital image is then divided into image blocks of the same size as thatused in the LBCE training method (602). The mean luminance value,blkMean, and the mean absolute luminance deviation, blkMAD, is thencomputed for each image block (604). The blkMean and blkMAD are computedin the same way as that used in the LBCE training method. For each imageblock, p control point values are retrieved from the p control pointtables using the blkMean and blkMAD for the image block and a local tonemapping curve is determined for the image block by interpolating the pcontrol points (606). In some embodiments of the invention, polynomialinterpolation such as cubic polynomial interpolation is used. The globaltone mapping curve is then merged with each of the local tone mappingcurves to generate merged tone mapping curves (607). Any suitabletechnique for merging the tone mapping curves may be used.

Once the merged tone mapping curves are determined, each pixel in theimage is enhanced based on the merged tone mapping curves (608-616). Apixel in the digital image is selected (608) and the distances from theselected pixel to the centers of q image blocks neighboring the imageblock containing the selected pixel are computed (610). The pixels maybe processed in any suitable order. In some embodiments of theinvention, the pixels are processed in raster scan order. The computeddistances are referred to as r1, . . . , rq herein.

The number of neighboring image blocks q may be empirically determined.In some embodiments of the invention, q=4. Which of the neighboringimage blocks used as the q neighboring image blocks may also beempirically determined. In some embodiments of the invention, theneighboring image blocks are the top neighboring image block, the leftneighboring image block, the right neighboring image block, and thebottom neighboring image block. If one of these neighboring image blocksdoes not exist, e.g., the image block containing the selected pixel isin a corner or along an edge of the digital image, then the neighboringimage block opposite the missing neighboring image block is used to fillin for the missing image block. For example, if an imaging block has atop neighboring block, a bottom neighboring block, and a rightneighboring block and the left neighboring block does not exist, theright neighboring block is used as both the right neighbor and the leftneighbor.

A brightness/contrast enhanced pixel value is computed for the selectedpixel based on the merged tone mapping curves of the q neighboring imageblocks and the distances r1, . . . , rq (612). First, the merged tonemapping curves computed for the q neighboring image blocks are appliedto the pixel to generate q tone mapped pixel values p1, . . . , pq.Then, the weighted average of the tone mapped pixel values p1, . . . ,pq is computed based on the distances r1, . . . , rq. The weightedaverage is the enhanced pixel value and is stored in the final image(612). The enhancement process is repeated for each pixel in the digitalimage (614).

The weights used in the weighted average may be computed as per thefollowing example. In this example, q=4 is assumed for simplicity ofexplanation. The equation (k/r1)+(k/r2)+(k/r3)+(k/r4)=1 is solved todetermine k. Note that this equation ensures that the weights used willadd up to 1. Then, the weights are computed as alfa1=k/r1, alfa2=k/r2,alfa3=k/r3, alfa4=k/r4 where alfa1 through alfa4 are the weights for p1,p2, p3, and p4. The weighted average is then computed asalfa1*p1+alfa2*p2+alfa3*p3+alfa4*p4.

Embodiments of the methods described herein may be provided on any ofseveral types of digital systems: digital signal processors (DSPs),general purpose programmable processors, application specific circuits,or systems on a chip (SoC) such as combinations of a DSP and a reducedinstruction set (RISC) processor together with various specializedprogrammable accelerators. A stored program in an onboard or external(flash EEP) ROM or FRAM may be used to implement the video signalprocessing. Analog-to-digital converters and digital-to-analogconverters provide coupling to the real world, modulators anddemodulators (plus antennas for air interfaces) can provide coupling fortransmission waveforms, and packetizers can provide formats fortransmission over networks such as the Internet.

The techniques described in this disclosure may be implemented inhardware, software, firmware, or any combination thereof. If implementedin software, the software may be executed in one or more processors,such as a microprocessor, application specific integrated circuit(ASIC), field programmable gate array (FPGA), or digital signalprocessor (DSP). The software that executes the techniques may beinitially stored in a computer-readable medium such as compact disc(CD), a diskette, a tape, a file, memory, or any other computer readablestorage device, and loaded and executed in the processor. In some cases,the software may also be sold in a computer program product, whichincludes the computer-readable medium and packaging materials for thecomputer-readable medium. In some cases, the software instructions maybe distributed via removable computer readable media (e.g., floppy disk,optical disk, flash memory, USB key), via a transmission path fromcomputer readable media on another digital system, etc.

Embodiments of the methods and encoders as described herein may beimplemented for virtually any type of digital system (e.g., a desk topcomputer, a laptop computer, a handheld device such as a mobile (i.e.,cellular) phone, a personal digital assistant, a digital camera, etc.)with functionality to capture and process digital images. FIGS. 7 and 8show block diagrams of illustrative digital systems.

FIG. 7 is a block diagram of a digital system (e.g., a mobile cellulartelephone) (900) that may be configured to apply BCE to digital imagesas described herein. The signal processing unit (SPU) (902) includes adigital signal processing system (DSP) that includes embedded memory andsecurity features. The analog baseband unit (904) receives a voice datastream from handset microphone (913 a) and sends a voice data stream tothe handset mono speaker (913 b). The analog baseband unit (904) alsoreceives a voice data stream from the microphone (914 a) and sends avoice data stream to the mono headset (914 b). The analog baseband unit(904) and the SPU (902) may be separate ICs. In many embodiments, theanalog baseband unit (904) does not embed a programmable processor core,but performs processing based on configuration of audio paths, filters,gains, etc being setup by software running on the SPU (902).

The display (920) may also display pictures and video sequences receivedfrom a local camera (928), or from other sources such as the USB (926)or the memory (912). The SPU (902) may also send a video sequence to thedisplay (920) that is received from various sources such as the cellularnetwork via the RF transceiver (906) or the camera (926). The SPU (902)may also send a video sequence to an external video display unit via theencoder unit (922) over a composite output terminal (924). The encoderunit (922) may provide encoding according to PAL/SECAM/NTSC videostandards.

The SPU (902) includes functionality to perform the computationaloperations required for video encoding and decoding. The video encodingstandards supported may include, for example, one or more of the JPEGstandards, the MPEG standards, and the H.26x standards. In one or moreembodiments of the invention, the SPU (902) is configured to performcomputational operations of BCE as described herein. Softwareinstructions implementing the BCE may be stored in the memory (912) andexecuted by the SPU (902), for example, as part of processing videosequences captured by the local camera (928).

FIG. 8 shows a digital system (800) (e.g., a personal computer) thatincludes a processor (802), associated memory (804), a storage device(806), and numerous other elements and functionalities typical ofdigital systems (not shown). In one or more embodiments of theinvention, a digital system may include multiple processors and/or oneor more of the processors may be digital signal processors. The digitalsystem (800) may also include input means, such as a keyboard (808) anda mouse (810) (or other cursor control device), and output means, suchas a monitor (812) (or other display device). Those skilled in the artwill appreciate that the input and output means may take other forms.The digital system (800) may also include an image capture device (notshown) that includes circuitry (e.g., optics, a sensor, readoutelectronics) for capturing video sequences. The digital system (800) maybe configured to apply BCE as described herein to the captured videosequences. The digital system (800) may be connected to a network (814)(e.g., a local area network (LAN), a wide area network (WAN) such as theInternet, a cellular network, any other similar type of network and/orany combination thereof) via a network interface connection (not shown).Encoded video sequences may be received by the digital system (800) viathe network and/or via a computer readable medium.

Further, those skilled in the art will appreciate that one or moreelements of the aforementioned digital system (800) may be located at aremote location and connected to the other elements over a network.Further, embodiments of the invention may be implemented on adistributed system having a plurality of nodes, where each portion ofthe system and software instructions may be located on a different nodewithin the distributed system. In one embodiment of the invention, thenode may be a digital system. Alternatively, the node may be a processorwith associated physical memory. The node may alternatively be aprocessor with shared memory and/or resources.

Software instructions to perform embodiments of the invention may bestored on a computer readable medium such as a compact disc (CD), adiskette, a tape, a file, memory, or any other computer readable storagedevice. The software instructions may be distributed to the digitalsystem (800) via removable computer readable media (e.g., floppy disk,optical disk, flash memory, USB key), via a transmission path fromcomputer readable media on another digital system, etc.

Embodiments of the methods described herein can be useful for enhancingor improving several types of images. Further, embodiments of themethods may be applied to images as they are captured (e.g., by adigital camera or scanner), as part of a photoprocessing application orother application with image processing capability executing on acomputer, and/or when the images are printed (e.g., in a printer as partof preparing to print the images). Embodiments of the methods may alsobe implemented as part of a device driver (e.g., a printer driver ordisplay driver), so that the driver performs BCE on an image before theimage is displayed or printed.

While the invention has been described with respect to a limited numberof embodiments, those skilled in the art, having benefit of thisdisclosure, will appreciate that other embodiments can be devised whichdo not depart from the scope of the invention as disclosed herein.Accordingly, the scope of the invention should be limited only by theattached claims. It is therefore contemplated that the appended claimswill cover any such modifications of the embodiments as fall within thetrue scope and spirit of the invention.

What is claimed is:
 1. A method for brightness and contrast enhancement (BCE) of a digital image, the method comprising: computing a luminance histogram of the digital image; computing first distances from the luminance histogram to a plurality of predetermined luminance histograms; estimating control point values for a global tone mapping curve for the digital image from first predetermined control point values corresponding to a subset of the predetermined luminance histograms selected based on the computed first distances; and interpolating the estimated control point values to determine the global tone mapping curve.
 2. The method of claim 1, further comprising applying the global tone mapping curve to the digital image to generate an enhanced digital image.
 3. The method of claim 1, wherein the first predetermined control point values comprise a vector of control point values for each predetermined luminance histogram, and wherein estimating first control point values comprises computing weighted averages of respective control point values in the vectors of control point values corresponding to the subset of the predetermined luminance histograms.
 4. The method of claim 1, wherein the predetermined luminance histograms and the first predetermined control points are predetermined by: computing luminance histograms for each training image in a plurality of training images; selecting a subset of the training images in the plurality of training images as a training prototype set based on distances from each luminance histogram to luminance histograms of training images in the training prototype set; and tuning each training image in the training prototype set based on a tuning tone mapping curve to generate the first predetermined control point values, wherein the luminance histograms of the training images in the training prototype set are the predetermined luminance histograms.
 5. The method of claim 4, further comprising applying principal component analysis to the luminance histograms of training images in the training prototype set.
 6. The method of claim 4, wherein tuning each training image is performed by a plurality of users and the first predetermined control points values are generated by computing an average of control point values selected by the plurality of users.
 7. The method of claim 1, further comprising: dividing the digital image into a plurality of image blocks; and enhancing pixels in the digital image to generate a final digital image, the enhancing comprising: computing second distances from a pixel to the centers of a predetermined number of neighboring image blocks of an image block comprising the pixel; computing an enhanced pixel value for the pixel based on the computed second distances, second predetermined control point values corresponding to the neighboring image blocks, and the global tone mapping curve; and storing the enhanced pixel value in the final digital image.
 8. The method of claim 7, further comprising: locating the second predetermined control point values corresponding to each image block of the neighboring image blocks using a mean luminance value and a mean absolute luminance deviation of the image block.
 9. The method of claim 7, wherein computing an enhanced pixel value comprises: generating a local tone mapping curve for each of the neighboring image blocks based on the second predetermined control point values; merging each local tone mapping curve with the global tone mapping curve to generate a merged tone mapping curve for each of the neighboring image blocks; applying each merged tone mapping curve to the pixel to generate tone mapped pixel values; and computing the enhanced pixel value as a weighted average of the tone mapped pixel values, wherein weights used in the weighted average are based on the second distances.
 10. The method of claim 7, wherein the predetermined luminance histograms, the first predetermined control points, and the second predetermined control points are predetermined by: computing luminance histograms for each training image in a plurality of training images; selecting a subset of the training images in the plurality of training images as a first training prototype set based on distances from each luminance histogram to luminance histograms of training images in the prototype set; tuning each training image in the first training prototype set based on a first tuning tone mapping curve to generate the first predetermined control point values, wherein the luminance histograms of the training images in the first training prototype set are the predetermined luminance histograms; enhancing each training image in the first training prototype set to generate enhanced training images, the enhancing comprising: computing third distances from the luminance histogram of a training image to the luminance histograms of all other training images in the first training prototype set; estimating third control point values for a tone mapping curve for the training image from first predetermined control point values corresponding to a subset of the luminance histograms of the other training images, the subset selected based on the computed third distances; interpolating the estimated third control point values to determine the tone mapping curve for the training image; and applying the tone mapping curve to the training image to generate an enhanced training image; dividing each enhanced training image into a plurality of image blocks to generate a second training prototype set of image blocks; computing a mean luminance value and a mean absolute luminance deviation value for each image block in the second training prototype set; and tuning each image block in the second training prototype set based on a second tuning tone mapping curve to generate the second predetermined control point values.
 11. The method of claim 10, further comprising applying principal component analysis to the luminance histograms of training images in the first training prototype set.
 12. The method of claim 10, wherein tuning each training image is performed by a plurality of users and the first predetermined control points values are generated by computing an average of control point values for each training image selected by the plurality of users, and wherein tuning each image block is performed by the plurality of users and the second predetermined control points values are generated by computing an average of control point values for each image block selected by the plurality of users.
 13. The method of claim 10, wherein the second predetermined control point values are stored based on the mean luminance values and the mean absolute luminance deviations of the image blocks.
 14. A digital system comprising: means for computing a luminance histogram of a digital image; means for computing first distances from the luminance histogram to a plurality of predetermined luminance histograms; means for estimating control point values for a global tone mapping curve for the digital image from first predetermined control point values corresponding to a subset of the predetermined luminance histograms selected based on the computed first distances; and means for interpolating the estimated control point values to determine the global tone mapping curve.
 15. The digital system of claim 14, further comprising means for applying the global tone mapping curve to the digital image to generate an enhanced digital image.
 16. The digital system of claim 14, wherein the predetermined luminance histograms and the first predetermined control points are predetermined by: computing luminance histograms for each training image in a plurality of training images; selecting a subset of the training images in the plurality of training images as a training prototype set based on distances from each luminance histogram to luminance histograms of training images in the training prototype set; and tuning each training image in the training prototype set based on a tuning tone mapping curve to generate the first predetermined control point values, wherein the luminance histograms of the training images in the training prototype set are the predetermined luminance histograms.
 17. The digital system of claim 16, wherein tuning each training image is performed by a plurality of users and the first predetermined control points values are generated by computing an average of control point values selected by the plurality of users.
 18. The digital system of claim 14, further comprising: means for dividing the digital image into a plurality of image blocks; means for enhancing pixels in the digital image to generate a final digital image, the means for enhancing comprising: means for computing second distances from a pixel to the centers of a predetermined number of neighboring image blocks of an image block comprising the pixel; means for computing an enhanced pixel value for the pixel based on the computed second distances, second predetermined control point values corresponding to the neighboring image blocks, and the global tone mapping curve; and means for storing the enhanced pixel value in the final digital image.
 19. The digital system of claim 18, wherein the means for computing an enhanced pixel value comprises: means for generating a local tone mapping curve for each of the neighboring image blocks based on the second predetermined control point values; means for merging each local tone mapping curve with the global tone mapping curve to generate a merged tone mapping curve for each of the neighboring image blocks; means for applying each merged tone mapping curve to the pixel to generate tone mapped pixel values; and means for computing the enhanced pixel value as a weighted average of the tone mapped pixel values, wherein weights used in the weighted average are based on the second distances.
 20. The digital system of claim 18, wherein the predetermined luminance histograms, the first predetermined control points, and the second predetermined control points are predetermined by: computing luminance histograms for each training image in a plurality of training images; selecting a subset of the training images in the plurality of training images as a first training prototype set based on distances from each luminance histogram to luminance histograms of training images in the prototype set; tuning each training image in the first training prototype set based on a first tuning tone mapping curve to generate the first predetermined control point values, wherein the luminance histograms of the training images in the first training prototype set are the predetermined luminance histograms; enhancing each training image in the first training prototype set to generate enhanced training images, the enhancing comprising: computing third distances from the luminance histogram of a training image to the luminance histograms of all other training images in the first training prototype set; estimating third control point values for a tone mapping curve for the training image from first predetermined control point values corresponding to a subset of the luminance histograms of the other training images, the subset selected based on the computed third distances; interpolating the estimated third control point values to determine the tone mapping curve for the training image; and applying the tone mapping curve to the training image to generate an enhanced training image; dividing each enhanced training image into a plurality of image blocks to generate a second training prototype set of image blocks; computing a mean luminance value and a mean absolute luminance deviation value for each image block in the second training prototype set; and tuning each image block in the second training prototype set based on a second tuning tone mapping curve to generate the second predetermined control point values. 